Introduction to Symbol Grounding
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A Different AI Scenario: AI and Justice in a Brave New World – Part 1
The recent upheavals at OpenAI and OpenAI’s Chief Scientist’s apprehensions regarding the “safety” of AI have ignited a fresh wave of concerns and fears about the march towards Artificial General Intelligence (AGI) and “Super Intelligence.” AI safety concerns the development of AI systems aligned with human values and do not cause harm to humans. Some… Read More »A Different AI Scenario: AI and Justice in a Brave New World – Part 1
The post A Different AI Scenario: AI and Justice in a Brave New World – Part 1 appeared first on Data Science Central.
( 22
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Moving Object Detection and Tracking with 4D Radar Point Cloud. (arXiv:2309.09737v2 [cs.CV] UPDATED)
Mobile autonomy relies on the precise perception of dynamic environments.
Robustly tracking moving objects in 3D world thus plays a pivotal role for
applications like trajectory prediction, obstacle avoidance, and path planning.
While most current methods utilize LiDARs or cameras for Multiple Object
Tracking (MOT), the capabilities of 4D imaging radars remain largely
unexplored. Recognizing the challenges posed by radar noise and point sparsity
in 4D radar data, we introduce RaTrack, an innovative solution tailored for
radar-based tracking. Bypassing the typical reliance on specific object types
and 3D bounding boxes, our method focuses on motion segmentation and
clustering, enriched by a motion estimation module. Evaluated on the
View-of-Delft dataset, RaTrack showcases superior tracking precision of moving
objects, largely surpassing the performance of the state of the art.
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Combining deep generative models with extreme value theory for synthetic hazard simulation: a multivariate and spatially coherent approach. (arXiv:2311.18521v1 [cs.LG])
Climate hazards can cause major disasters when they occur simultaneously as
compound hazards. To understand the distribution of climate risk and inform
adaptation policies, scientists need to simulate a large number of physically
realistic and spatially coherent events. Current methods are limited by
computational constraints and the probabilistic spatial distribution of
compound events is not given sufficient attention. The bottleneck in current
approaches lies in modelling the dependence structure between variables, as
inference on parametric models suffers from the curse of dimensionality.
Generative adversarial networks (GANs) are well-suited to such a problem due to
their ability to implicitly learn the distribution of data in high-dimensional
settings. We employ a GAN to model the dependence structure for daily maximum
wind speed, significant wave height, and total precipitation over the Bay of
Bengal, combining this with traditional extreme value theory for controlled
extrapolation of the tails. Once trained, the model can be used to efficiently
generate thousands of realistic compound hazard events, which can inform
climate risk assessments for climate adaptation and disaster preparedness. The
method developed is flexible and transferable to other multivariate and spatial
climate datasets.
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Fair Community Detection and Structure Learning in Heterogeneous Graphical Models. (arXiv:2112.05128v2 [stat.ML] UPDATED)
Inference of community structure in probabilistic graphical models may not be
consistent with fairness constraints when nodes have demographic attributes.
Certain demographics may be over-represented in some detected communities and
under-represented in others. This paper defines a novel $\ell_1$-regularized
pseudo-likelihood approach for fair graphical model selection. In particular,
we assume there is some community or clustering structure in the true
underlying graph, and we seek to learn a sparse undirected graph and its
communities from the data such that demographic groups are fairly represented
within the communities. In the case when the graph is known a priori, we
provide a convex semidefinite programming approach for fair community
detection. We establish the statistical consistency of the proposed method for
both a Gaussian graphical model and an Ising model for, respectively,
continuous and binary data, proving that our method can recover the graphs and
their fair communities with high probability.
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Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics. (arXiv:2305.02381v2 [cs.SI] UPDATED)
Analyzing large-scale time-series network data, such as social media and
email communications, poses a significant challenge in understanding social
dynamics, detecting anomalies, and predicting trends. In particular, the
scalability of graph analysis is a critical hurdle impeding progress in
large-scale downstream inference. To address this challenge, we introduce a
temporal encoder embedding method. This approach leverages ground-truth or
estimated vertex labels, enabling an efficient embedding of large-scale graph
data and the processing of billions of edges within minutes. Furthermore, this
embedding unveils a temporal dynamic statistic capable of detecting
communication pattern shifts across all levels, ranging from individual
vertices to vertex communities and the overall graph structure. We provide
theoretical support to confirm its soundness under random graph models, and
demonstrate its numerical advantages in capturing evolving communities and
identifying outliers. Finally, we showcase the practical application of our
approach by analyzing an anonymized time-series communication network from a
large organization spanning 2019-2020, enabling us to assess the impact of
Covid-19 on workplace communication patterns.
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The Sliding Regret in Stochastic Bandits: Discriminating Index and Randomized Policies. (arXiv:2311.18437v1 [cs.LG])
This paper studies the one-shot behavior of no-regret algorithms for
stochastic bandits. Although many algorithms are known to be asymptotically
optimal with respect to the expected regret, over a single run, their
pseudo-regret seems to follow one of two tendencies: it is either smooth or
bumpy. To measure this tendency, we introduce a new notion: the sliding regret,
that measures the worst pseudo-regret over a time-window of fixed length
sliding to infinity. We show that randomized methods (e.g. Thompson Sampling
and MED) have optimal sliding regret, while index policies, although possibly
asymptotically optimal for the expected regret, have the worst possible sliding
regret under regularity conditions on their index (e.g. UCB, UCB-V, KL-UCB,
MOSS, IMED etc.). We further analyze the average bumpiness of the pseudo-regret
of index policies via the regret of exploration, that we show to be suboptimal
as well.
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Some Intriguing Aspects about Lipschitz Continuity of Neural Networks. (arXiv:2302.10886v3 [cs.LG] UPDATED)
Lipschitz continuity is a crucial functional property of any predictive
model, that naturally governs its robustness, generalisation, as well as
adversarial vulnerability. Contrary to other works that focus on obtaining
tighter bounds and developing different practical strategies to enforce certain
Lipschitz properties, we aim to thoroughly examine and characterise the
Lipschitz behaviour of Neural Networks. Thus, we carry out an empirical
investigation in a range of different settings (namely, architectures,
datasets, label noise, and more) by exhausting the limits of the simplest and
the most general lower and upper bounds. As a highlight of this investigation,
we showcase a remarkable fidelity of the lower Lipschitz bound, identify a
striking Double Descent trend in both upper and lower bounds to the Lipschitz
and explain the intriguing effects of label noise on function smoothness and
generalisation.
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Choosing the parameter of the Fermat distance: navigating geometry and noise. (arXiv:2311.18663v1 [stat.ML])
The Fermat distance has been recently established as a useful tool for
machine learning tasks when a natural distance is not directly available to the
practitioner or to improve the results given by Euclidean distances by
exploding the geometrical and statistical properties of the dataset. This
distance depends on a parameter $\alpha$ that greatly impacts the performance
of subsequent tasks. Ideally, the value of $\alpha$ should be large enough to
navigate the geometric intricacies inherent to the problem. At the same, it
should remain restrained enough to sidestep any deleterious ramifications
stemming from noise during the process of distance estimation. We study both
theoretically and through simulations how to select this parameter.
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A Survey of Learning Criteria Going Beyond the Usual Risk. (arXiv:2110.04996v3 [stat.ML] UPDATED)
Virtually all machine learning tasks are characterized using some form of
loss function, and "good performance" is typically stated in terms of a
sufficiently small average loss, taken over the random draw of test data. While
optimizing for performance on average is intuitive, convenient to analyze in
theory, and easy to implement in practice, such a choice brings about
trade-offs. In this work, we survey and introduce a wide variety of
non-traditional criteria used to design and evaluate machine learning
algorithms, place the classical paradigm within the proper historical context,
and propose a view of learning problems which emphasizes the question of "what
makes for a desirable loss distribution?" in place of tacit use of the expected
loss.
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$\mathbb{Z}_2\times \mathbb{Z}_2$ Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks. (arXiv:2311.18744v1 [quant-ph])
This paper presents a comprehensive comparative analysis of the performance
of Equivariant Quantum Neural Networks (EQNN) and Quantum Neural Networks
(QNN), juxtaposed against their classical counterparts: Equivariant Neural
Networks (ENN) and Deep Neural Networks (DNN). We evaluate the performance of
each network with two toy examples for a binary classification task, focusing
on model complexity (measured by the number of parameters) and the size of the
training data set. Our results show that the $\mathbb{Z}_2\times \mathbb{Z}_2$
EQNN and the QNN provide superior performance for smaller parameter sets and
modest training data samples.
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